Anomaly analysis is of great interest to diverse fields, including data mining and machine
learning, and plays a critical role in a wide range of applications, such as medical health …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …

It is obvious to see that most of the datasets do not have exactly equal number of samples for
each class. However, there are some tasks like detection of fraudulent transactions, for …

Searching for an efficient and reliable method to reduce manual intervention and subjective
parameter selection during the discontinuity characterization process of rock tunnel faces is …

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue
in machine learning. Identifying anomalous objects from given data has a broad range of …

Outliers has been studied in a variety of domains including Big Data, High dimensional data,
Uncertain data, Time Series data, Biological data, etc. In majority of the sample datasets …

Steady‐state forecasting is indispensable for power system planning and operation. A
forecasting model for inputs considering their historical record is a preliminary step for such …

Outlier detection is a hot topic in machine learning. With the newly emerging technologies
and diverse applications, the interest of outlier detection is increasing greatly. Recently, a …